On-line monitoring of a sugar crystallization process

Simoglou, A.; Georgieva, Pétia; Martin, E.B.; Morris, A.J.; Azevedo, S. Feyo de

doi:10.1016/j.compchemeng.2005.02.013

Cited by 53 publications

(28 citation statements)

References 11 publications

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“…Models that combine analytical and data-driven approach to approximate the batch crystallization phenomenon have been developed in Ref. [9] (see also the contribution by Simoglou et al [10]). …”

Section: Introductionmentioning

confidence: 99%

Dynamical model of brushite precipitation

Oliveira¹,

Georgieva²,

Rocha³

et al. 2007

Journal of Crystal Growth

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Section: Introductionmentioning

confidence: 99%

Dynamical model of brushite precipitation

Oliveira¹,

Georgieva²,

Rocha³

et al. 2007

Journal of Crystal Growth

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“…The general phenomenological model of the fed-batch crystallization process consists of mass, energy and population balances, including the relevant kinetic rates for nucleation, linear growth and agglomeration (Simoglou et al, 2005). While the mass and energy balances are common expressions in many chemical process models, the population balance is related with the crystallization phenomenon, which is still an open modelling problem.…”

Section: Crystallization Modelmentioning

confidence: 99%

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Suarez¹,

Georgieva²,

Azevedo³

2011

Advanced Model Predictive Control

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“…In chemical engineering applications, data driven FDD has been applied to crystallization [21] , fermentation processes, continuous stirred tank reactor [22,24,26] and waste water treatment plant [27] . Successful applications have also been made to the benchmark Eastman processes [9,12,28] .…”

Section: Applications Of Multi-statistics Based Fddmentioning

confidence: 99%

Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions

Wang

Chai

Ding

et al. 2009

Acta Automatica Sinica

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This paper presents a selected survey covering the advances of fault diagnosis and fault tolerant control using data driven techniques. A brief summary of the general developments in fault detection and diagnosis for industrial processes is given, which is then followed by discussions on the widely used data driven and knowledge-based techniques. A successful application example is also given, which deals with faults caused by the misplacement of control loop set points and several areas of potential future directions are included in the paper. Key words Fault detection, fault diagnosis, fault tolerant control, data driven techniques Fault detection and diagnosis (FDD) and fault tolerant control (FTC) have been the subject of considerable interest in the control research community [1−39] . This is in response to the ever increasing requirements on the reliable operation of control systems, which are, in most cases, subject to a number of faults either in the internal closed loops or from environmental factors. Once system faults have occurred, they can cause unrecoverable losses and result in unacceptable environmental pollution, etc. Occasionally, the occurrence of a minor fault has resulted in disastrous effects. For example, it has been observed that faults have caused a 3 % ∼ 8 % reduction of the oil production in the United States, leading to $ 20 billion losses in the country s economy per year. Also, in 1997, the faults in a chemical plant in Beijing caused heavy direct losses. Therefore, effective FDD is of vital importance to the safe operation of industrial plants. Indeed, FDD and FTC have now become an integral part of industrial process control.In general, system faults can be grouped into several categories, namely, actuator faults, sensor faults, system faults and also abnormal operating faults caused by either the misplacement of control loop set points or unexpected variations in the raw materials to be processed. The purpose of FDD is to use available signals to detect, identify, and isolate possible sensor faults, actuator faults, and system faults. Conversely, FTC calculates the required actions (either controller modification or reconfiguration) so that the system can still continue to operate safely even under faulty conditions [2][3]40] . In terms of condition monitoring or FDD, the existing methods can also be grouped into the following two categories: 1) Model based FDD; 2) Data driven FDD including knowledge based FDD. In the early days (1980 s onwards), model based FDD constituted the main stream of research, and a number of techniques were developed. Depending on whether the system model can be represented as either a state space model or an input-output model, FDD can be classified into two groups: observer based FDD [1] and system identification based FDD [4] . Also, to combine the best features of these two approaches, there is another group of FDD methods called adaptive observer based fault diagnosis, which uses parameter tuning principles from model reference adap- tive ...

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On-line monitoring of a sugar crystallization process

Cited by 53 publications

References 11 publications

Dynamical model of brushite precipitation

Dynamical model of brushite precipitation

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions

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